All it took was a new formula for crunching data from satellites and ground-based instruments to dramatically reduce the amount of computing power needed to achieve highly-precise GPS tracking, according to a Feb. 10 press release from the University of California, Riverside. Such technology could mean easy tracking of autonomous vehicles within lanes, for instance.
“To fulfill both the automation and safety needs of driverless cars, some applications need to know not only which lane a car is in, but also where it is in that lane — and need to know it continuously at high rates and high bandwidth for the duration of the trip,” UCR Chair of Electrical and Computer Engineering Jay Farrell said in the statement.
A necessary component of self-driving vehicles — especially those designed to drive without any human inside, such as Google’s patented autonomous delivery trucks — is the ability of vehicles to navigate along a pre-planned route. That means using a location tracking system such as GPS.
Currently standard GPS is only accurate to within 10 meters of an object’s actual location, and advanced GPS is accurate up to a meter. By connecting satellites with ground-based devices such as an intertial measurement unit in a car, and using Farrell’s method for calculating position based on that data, he said GPS can become much more accurate without requiring a huge investment in computation.
The potential applications extend outside autonomous vehicles, he said in the statement. Precision location tracking could improve navigation for aircraft and sea vessels, smart phones and wearable technology — all without requiring cost increases.
Farrell and a team of researchers have published their methods in the journal “Transactions on Control Systems Technology” and are applying for a patent for the idea.
